Minimizing the volumes of confidence ellipsoids in this setting as is done in [1]} is referred to as maximum likelihood “D”-optimality since it entails maximizing \(\log \det {\left({\mathbf {M}}_{{S}}^T{\mathbf {C}}_{{\mathbf {n}}_{{S}}}^{-1}{\mathbf {M}}_{{S}}\right)}\) .
In the absence of the regularizing effect the prior distribution has on the estimate, we must have at least as many sensor measurements as state variables in the maximum likelihood setting.
